85 lines
4 KiB
Python
85 lines
4 KiB
Python
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# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
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import torch
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from loguru import logger
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import torch.nn as nn
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import torch.nn.functional as F
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from .latent_points_ada import PVCNN2Unet
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from .utils import mask_inactive_variables
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# diffusion model for latent points
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class PVCNN2Prior(PVCNN2Unet):
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sa_blocks = [ # conv_configs, sa_configs
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((32, 2, 32), (1024, 0.1, 32, (32, 64))),
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((64, 3, 16), (256, 0.2, 32, (64, 128))),
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((128, 3, 8), (64, 0.4, 32, (128, 128))),
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(None, (16, 0.8, 32, (128, 128, 128))),
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]
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fp_blocks = [
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((128, 128), (128, 3, 8)), # fp_configs, conv_configs
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((128, 128), (128, 3, 8)),
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((128, 128), (128, 2, 16)),
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((128, 128, 64), (64, 2, 32)),
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]
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def __init__(self, args, num_input_channels, cfg):
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# only cfg is used
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self.clip_forge_enable = cfg.clipforge.enable
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clip_forge_dim = cfg.clipforge.feat_dim
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num_input_channels = num_classes = cfg.shapelatent.latent_dim + cfg.ddpm.input_dim
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self.num_classes = num_classes
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embed_dim = cfg.ddpm.time_dim
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use_att = True
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extra_feature_channels = cfg.shapelatent.latent_dim
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self.num_points = cfg.data.tr_max_sample_points
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dropout = cfg.ddpm.dropout
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time_emb_scales = cfg.sde.embedding_scale # 1k default
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logger.info('[Build Prior Model] nclass={}, embed_dim={}, use_att={},'
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'extra_feature_channels={}, dropout={}, time_emb_scales={} num_point={}',
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num_classes, embed_dim, use_att, extra_feature_channels, dropout, time_emb_scales,
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self.num_points)
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# Attention: we are not using time_emb_scales here, but the embedding_scale
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super().__init__(
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num_classes, embed_dim, use_att, dropout=dropout,
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input_dim=cfg.ddpm.input_dim,
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extra_feature_channels=extra_feature_channels,
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time_emb_scales=time_emb_scales,
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verbose=True,
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condition_input=False,
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cfg=cfg,
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sa_blocks=self.sa_blocks,
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fp_blocks=self.fp_blocks,
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clip_forge_enable=self.clip_forge_enable, clip_forge_dim=clip_forge_dim)
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# init mixing logit
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self.mixed_prediction = cfg.sde.mixed_prediction # This enables mixed prediction
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if self.mixed_prediction:
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logger.info('init-mixing_logit = {}, after sigmoid = {}',
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cfg.sde.mixing_logit_init, torch.sigmoid(torch.tensor(cfg.sde.mixing_logit_init))
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)
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init = cfg.sde.mixing_logit_init * torch.ones(size=[1, num_input_channels*self.num_points, 1, 1])
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self.mixing_logit = torch.nn.Parameter(init, requires_grad=True)
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self.is_active = None
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else: # no mixing_logit
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self.mixing_logit = None
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self.is_active = None
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def forward(self, x, t, *args, **kwargs): #x0=None):
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# Input: x: B,ND or B,ND,1,1
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# require shape for x: B,C,N
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## CHECKEQ(x.shape[-1], self.num_classes)
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assert('condition_input' in kwargs), 'require condition_input'
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if self.mixed_prediction and self.is_active is not None:
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x = mask_inactive_variables(x, self.is_active)
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input_shape = x.shape
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x = x.view(-1,self.num_points,self.num_classes).permute(0,2,1).contiguous()
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B = x.shape[0]
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out = super().forward(x, t=t, style=kwargs['condition_input'].squeeze(-1).squeeze(-1), clip_feat=kwargs.get('clip_feat', None))
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return out.permute(0,2,1).contiguous().view(input_shape)
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# -1,self.num_classes) # BDN -> BND -> BN,D
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